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A Knowledge Graph-Based Framework for Smart Home Device Action Recommendation and Demand Response

Author

Listed:
  • Wenzhi Chen

    (The Department of Engineering, Durham University, Durham DH1 3LE, UK)

  • Hongjian Sun

    (The Department of Engineering, Durham University, Durham DH1 3LE, UK)

  • Minglei You

    (The Department of Electrical and Electronic Engineering, University of Nottingham, Nottingham NG8 1BB, UK)

  • Jing Jiang

    (The Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK)

  • Marco Rivera

    (The Department of Electrical and Electronic Engineering, University of Nottingham, Nottingham NG8 1BB, UK)

Abstract

Within smart homes, consumers could generate a vast amount of data that, if analyzed effectively, can improve the convenience of consumers and reduce energy consumption. In this paper, we propose to organize household appliance data into a knowledge graph by using the consumers’ usage habits, the periods of usage, and the location information for graph modeling. A framework, ‘DARK’ (Device Action Recommendation with Knowledge graphs), is proposed that includes three parts for enabling demand response. Firstly, a household device action recommendation algorithm is proposed that improves the knowledge graph attention algorithm to make accurate household appliance recommendations. Secondly, graph interpretable characteristics are developed in the DARK using trained graph embeddings. Finally, with the recommendation expectation, the consumers’ comfort level and appliances’ average power load are modeled as a multi-objective optimization problem in the DARK to participate in demand response. The results demonstrate that the proposed system can generate appliances’ action recommendations with an average of 93.4% accuracy and reduce power load by up to 20% while providing reasonable interpretations for the device action recommendation results on the customized UK-DALE dataset.

Suggested Citation

  • Wenzhi Chen & Hongjian Sun & Minglei You & Jing Jiang & Marco Rivera, 2025. "A Knowledge Graph-Based Framework for Smart Home Device Action Recommendation and Demand Response," Energies, MDPI, vol. 18(4), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:4:p:833-:d:1588433
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    References listed on IDEAS

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    4. Tahar Dilekh & Saber Benharzallah & Ayoub Mokeddem & Saoueb Kerdoudi, 2024. "Dynamic Context-Aware Recommender System for Home Automation Through Synergistic Unsupervised and Supervised Learning Algorithms," Acta Informatica Pragensia, Prague University of Economics and Business, vol. 2024(1), pages 38-61.
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